Quantifying the impact of systemic bias on careers: gender differences in science & art
NetSI Speaker Series
Alex Gates
Associate Research Scientist, CCNR
Past Talk
Tuesday
Nov 24, 2020
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2:00 pm
Virtual
177 Huntington Ave.
11th floor
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Attempts to quantify the influence of systemic inequality on careers are hindered by a complex interplay between multiple factors: individual success is not just grounded in individual performance, but is largely impacted by institutional practices that can limit the access for some individuals to future opportunities, and offer a winners-take-all outcome for others.  These interlocking effects suggest that, to truly understand the impact of systemic bias on careers, it is not enough to focus on selected institutions or single metrics, but we need to map entire ecosystems to disentangle individual performance from the structural patterns that reinforce systematic bias.  Here, we explore systemic gender differences in science & art.  First, we examine scientific careers where we identify significant gender differences in total productivity and impact across STEM fields.  Paradoxically, we find that the increase in the number of women academics over the past 60 years has increased these gender differences.  Yet, men and women publish a comparable number of papers per year and have equivalent career-wise impact for the same total number of publications.  Through comprehensive matching quasi-experiments, we show that productivity and impact gender differences are largely explained by different publishing career lengths and dropout rates.  Second, we leverage a massive longitudinal dataset capturing the exhibition and auction careers of artists.  We confirm gender differences in population and access to exhibitions, an effect that increases for more prestigious institutions.  Using the global population gender imbalance as a new baseline, we develop a measure of institutional bias that, combined with the network of artist trajectories, reveals systematic echo-chambers which limit the access for some artists to future exhibition opportunities and induces a strong gender difference in the access to the secondary auction market.  Taken together, this quantitative perspective of gender equality in science and art suggests that it is not enough to just increase the participation of women, we must break down the ``glass fences'' which systematically impede success based on gender.

About the speaker
About the speaker
Alexander Gates is an Associate Research Scientist at the Center for Complex Network Research (CCNR). He currently works with professor Barabási on the science of success and the dynamics of academic careers. Before arriving at Northeastern, Alex received a joint PhD degree in Informatics (complex systems track) and Cognitive Science from Indiana University, Bloomington, an MSc from Kings College London in complex systems modeling and a BA in mathematics from Cornell University. His academic research fuses mathematical and computational methods to study complex systems in biology, neuroscience, and sociology. Some of his recent contributions include a systematic quantification of control in gene regulatory networks, a dynamical protocell model for autopoiesis, and a novel framework for comparing overlapping and hierarchical clusters and communities.
Alexander Gates is an Associate Research Scientist at the Center for Complex Network Research (CCNR). He currently works with professor Barabási on the science of success and the dynamics of academic careers. Before arriving at Northeastern, Alex received a joint PhD degree in Informatics (complex systems track) and Cognitive Science from Indiana University, Bloomington, an MSc from Kings College London in complex systems modeling and a BA in mathematics from Cornell University. His academic research fuses mathematical and computational methods to study complex systems in biology, neuroscience, and sociology. Some of his recent contributions include a systematic quantification of control in gene regulatory networks, a dynamical protocell model for autopoiesis, and a novel framework for comparing overlapping and hierarchical clusters and communities.